Self-adaptation via multi-objectivisation
Self-adaptation via multi-objectivisation
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通过多目标化进行自适应
DOI：
10.1145/3512290.3528836
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Lehre P
中科院分区：
文献类型：
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作者：
Lehre P
探索与利用的困境是在探索新的但可能不太适合的适应度区域的同时，也关注最适合的个体附近的区域。对于可调问题类SparseLocalOpt，具有锦标赛选择的非精英EA可以使用足够高的突变率来限制群体中“稀疏”局部最优个体的百分比（Dang等人，2021年）。然而，EA的性能关键取决于选择“正确的”突变率，这是特定于问题实例的。一个有前途的方法是自适应，其中参数设置编码在染色体和evolved.We提出了一个新的自适应EA单目标优化，它对待参数控制的角度来看，多目标优化：该算法同时最大化的适应度和变异率。由于处于“密集”适应值谷的个体具有较高的变异率，而处于“稀疏”局部最优的个体只有较低的变异率，因此它们可以共存于一个非支配的Pareto前沿上.互补实验结果表明，MOSA-EA优于随机NK景观和k-Sat实例的EA的范围。
The exploration vs exploitation dilemma is to balance exploring new but potentially less fit regions of the fitness landscape while also focusing on regions near the fittest individuals. For the tunable problem class SparseLocalOpt, a non-elitist EA with tournament selection can limit the percentage of "sparse" local optimal individuals in the population using a sufficiently high mutation rate (Dang et al., 2021). However, the performance of the EA depends critically on choosing the "right" mutation rate, which is problem instance-specific. A promising approach is self-adaptation, where parameter settings are encoded in chromosomes and evolved.We propose a new self-adaptive EA for single-objective optimisation, which treats parameter control from the perspective of multiobjective optimisation: The algorithm simultaneously maximises the fitness and the mutation rates. Since individuals in "dense" fitness valleys survive high mutation rates, and individuals on "sparse" local optima only survive with lower mutation rates, they can coexist on a non-dominated Pareto front.Runtime analyses show that this new algorithm (MOSA-EA) can efficiently escape a local optimum with unknown sparsity, where some fixed mutation rate EAs become trapped. Complementary experimental results show that the MOSA-EA outperforms a range of EAs on random NK-Landscape andk-Sat instances.